Emmanuel Adams

Key Skills

Software

Appen

HiveMind

Mindrift

Other

Labelbox

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Video

Top Task Types

Audio Recording

Computer Programming Coding

Diagnosis

Text Generation

Translation Localization

Freelancer Overview

Highly experienced and detail-oriented Data Annotator with 3+ of proven success in generating high-quality training datasets for diverse Artificial Intelligence and Machine Learning applications. Proficient in a wide array of annotation techniques including bounding boxes, semantic segmentation, keypoint annotation, text classification, and sentiment analysis. My expertise spans various data modalities such as images, videos, text, and audio. I am adept at utilizing industry-standard annotation platforms like Appen, Labelbox, SuperAnnotate, Mindrift and quickly adapting to new proprietary tooling. Committed to delivering meticulously labeled data that adheres to strict guidelines, directly contributing to the accuracy and performance of AI models. I possess strong analytical skills, a keen eye for detail, and excellent communication to ensure seamless collaboration on complex projects. With 3+ years of dedicated experience in data labeling and annotation, I provide high-quality, precise training data crucial for robust AI/ML model development. My expertise spans image, text, and video annotation, utilizing tools like Labelbox and Mindrift for tasks such as bounding box detection, semantic segmentation, and sentiment analysis. I am highly detail-oriented, committed to adhering to strict guidelines, and possess a strong understanding of how accurate data labeling directly impacts model performance.

Labeling Experience

Human Feedback for Large Language Model Alignment (RLHF)

Internal Proprietary ToolingTextRLHF

Critically evaluated and ranked thousands of AI-generated responses based on strict criteria for factual accuracy, helpfulness, and harmlessness. Identified and provided detailed feedback on instances of bias, hallucination, or inappropriate content. My contributions directly aided in the fine-tuning of a major LLM, significantly improving its safety, reliability, and alignment with user expectations.

Critically evaluated and ranked thousands of AI-generated responses based on strict criteria for factual accuracy, helpfulness, and harmlessness. Identified and provided detailed feedback on instances of bias, hallucination, or inappropriate content. My contributions directly aided in the fine-tuning of a major LLM, significantly improving its safety, reliability, and alignment with user expectations.

2022 - 2023

High-Precision Object Annotation for Autonomous Driving Perception

LabelboxImageBounding BoxPolygon

Meticulously annotated thousands of complex image and video frames to delineate vehicles, pedestrians, traffic signs, and road features with pixel-level accuracy. Executed 3D cuboid annotation on LiDAR-fused data for precise spatial understanding. Ensured high inter-annotator agreement (98%+) by rigorously following detailed guidelines, providing crucial training data for robust self-driving perception systems operating in diverse conditions.

Meticulously annotated thousands of complex image and video frames to delineate vehicles, pedestrians, traffic signs, and road features with pixel-level accuracy. Executed 3D cuboid annotation on LiDAR-fused data for precise spatial understanding. Ensured high inter-annotator agreement (98%+) by rigorously following detailed guidelines, providing crucial training data for robust self-driving perception systems operating in diverse conditions.

2022 - 2022

Education

Work History